{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (274847989)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dmzdv1qc+9am01pCqdPfMza688kq7S3AtjjNz6Jd59Mw8euZPngjbHR0dysjIUHZ2tnp6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\ntn/84x9r/vz5+uxnP6vOzk7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sqqoqBt4AAwz9ltx2223q6OhQU1OTjh8/nvj/2267zer6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\nwCaEbQAAAMAmhG0AAADAJn8Dy7K+LjzqF+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103a3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(chopsticks, aes(x='individual', y='food_pinching_effeciency')) + geom_point()"
   ]
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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